1. Field of the Invention
The present invention concerns a method for surface contouring of a three-dimensional image of a subject.
2. Description of the Prior Art
For various applications (for example photographic analysis, industrial image processing, monitoring, traffic systems, media technology and animation), image processing and object recognition of two-dimensional, and to a certain extent three-dimensional, images of objects have progressed relatively far. The image data sets forming the images generally are relatively undisturbed and are acquired, for example, with optical sensors (see, for example, Inspect. MessTecAutomation, special publication 10/01, GIT Publishing Darmstadt, 2001, or Vision Systems Design, Vol. 6 #9, Pennwell, September 2001).
If the image data sets are relatively undisturbed, the surface contours of the images of the objects can be relatively simply determined by means of known methods. With linear or nonlinear filtering, noise in the image data sets can be further reduced. In order to recognize textures of the images, pattern matching can be implemented. Object borders can be determined, for example, by 2D- or 3D gradient calculation. Techniques based on gradient calculation are, for example, “active contours”, specified in Chalana V., Linker D., “A Multiple Active Contour Model for Cardiac Boundary Detection on Echocardiographic Sequences”, IEEE Trans. Med. Imaging 15, #3, June 1996, or so-called as “snakes”, specified in McInerney T, Terzopoulos D: “T-snakes: Topology Adaptive Snakes”, Medical Image Analysis 4, pages 73-91, 2000. Imaged objects of the same brightness, color or texture can, on the other hand, be “filled out”, for example by a technique known as region growing, and thus be completely described for a specific task (see Jendrysiak U., “Segmentierung von Schnittbildern”. Spektrum der Wissenschaft, Dossier January 1999: Perspektiven in der Medizintechnik 24-29, 1999).
In medical technology, however, it may be the case that imaged healthy and diseased tissue exhibit a relatively small contrast difference, and image data sets are very noisy. This is true in particular for x-ray images (computed tomography, angiography, fluoroscopy), images produced with a magnetic resonance device or in nuclear medicine, and particularly for images based on ultrasound (compare Marais P., Brady J., “Detecting the Brain Surface in Sparse MRI Using Boundary Models”, Medical Image Analysis 4, 283-302, 2000, Chalana V., Linker D., “A Multiple Active Contour Model for Cardiac Boundary Detection on Echocardiographic Sequences”, IEEE Trans. Med. Imaging 15, #3, June 1996, or Sakas G., “Dreidimensionale Bildrekonstruktion aus Ultraschall-Daten”, Spektrum der Wissenschaft, Dossier January 1999: Perspektiven in der Medizintechnik 18-24, 1999).
Particularly when searching for a diagnosis for a patient, fine object differences must be differentiated in the overall depth of the body or of the organs of the patient. Since the image data sets acquired with medical devices often are very noisy, and acquired structures can be relatively complex, a determination of a contour in a two-dimensional image is already relatively computation-intensive. The determination of a contour of a three-dimensional image of an object is even more difficult. Known methods therefore have been refined, and often adapted ad hoc to the present problem. An example is specified in Chalana V., Linker D., “A Multiple Active Contour Model for Cardiac Boundary Detection on Echocardiographic Sequences”, IEEE Trans. Med. Imaging 15, #3, June 1996. Another relatively computation-intensive method is specified in Jendrysiak U., “Segmentierung von Schnittbildern”. Spektrum der Wissenschaft, Dossier January 1999: Perspektiven in der Medizintechnik 24-29, 1999.